Knowledge Engines for Critical Decision Support

Abstract

Current knowledge capture and retention techniques tend to codify “what-is” and “who knows” more effectively than “how-to”. Unfortunately, “how-to” knowledge is more directly actionable, and indispensable for critical organizational activities such as strategic analysis and decision-making. KM theorists often despair over “how-to” expertise as a form of tacit knowledge that is difficult to articulate, much less transfer. We argue that tacit strategic performance-based knowledge can often be captured and deployed effectively, via frameworks that combine scenario planning methods with “what-if” simulation. The key challenges are two-fold: (1) modeling complex situational contexts, including known behavioral dynamics; and (2) enabling knowledge workers to manipulate such models interactively, to safely practice situational analysis and decision-making and learn from virtual rather real mistakes. We illustrate our approach with example knowledge-based decision support solutions and provide pointers to related literature.